Financial Data Representation and Similarity Model

Abstract

Abstract-Similarity metric is of fundamental importance for similarity matching and subsequence query in time series applications. Most existing approaches measure the similarity by calculating and aggregating the point-to-point distance, few of them take the segment trend duration into account. In this paper, upon analyzing the properties of financial time series, we define a time series notation which is more intuitive and expressive. Base on that, a new similarity model is proposed. Experiments on both real foreign currency exchange rate data and stock market data are performed. The result shows the effectiveness and good accuracy of our method. The similarity model is also proved to be segmentation algorithm independent thus can be combined with other segmentations for similarity query, pattern matching, classification, and clustering.

From the currency rate quotations onto strings and brane world scenarios

“…Here we work on the concept that is based on projection data into higher dimensional vectors in the sense of the work [7,8]. Also, arguments based on the metrics are consistent with our efforts but not too obvious points in common with the original objectives of the nonlinear analysis.…”

“…(7), we obtainP ab q (τ, h) = f q p bid (τ + h) − p ask (τ ) p(τ + h) p bid (τ + l s ) − p ask (τ + h) p(τ + l s ) . (35)However this clearly violates, the Dirichlet boundary conditions, Eq (8)…”

The string prediction models as invariants of time series in the forex market

“…It is the relationship between more intuitive geometric methods and financial data. Here we work on the concept that is based on projection data into higher dimensional vectors in the sense of the works [15,16].…”